cdmAmsIIe

cdmAmsIIe implements the Clean Development Mechanism (CDM) small-scale methodology AMS-II.E Energy efficiency and fuel switching measures for buildings.

The package follows tidyverse design principles and exposes equation-level helpers, applicability checks, and meta-calculation wrappers to reproduce emission reduction estimates for building energy efficiency interventions.

Installation

# install.packages("devtools")
devtools::install_github("independent-impact/GHG_methodologies/cdmAmsIIe")

Getting Started

library(cdmAmsIIe)

monitoring <- simulate_ams_iie_dataset(n_buildings = 2, n_periods = 6)
annual_monitoring <- aggregate_monitoring_periods(
  monitoring,
  period_col = monitoring_label,
  group_cols = "building_id"
)

applicability <- assess_ams_iie_applicability(
  baseline_data = annual_monitoring,
  project_data = annual_monitoring,
  monitoring_data = annual_monitoring
)

if (applicability$overall_applicable) {
  baseline <- calculate_baseline_building_emissions(annual_monitoring, group_cols = "building_id")
  project <- calculate_project_building_emissions(annual_monitoring, group_cols = "building_id")
  leakage <- calculate_leakage_emissions(annual_monitoring,
    group_cols = "building_id",
    leakage_col = "leakage_emissions_tco2e"
  )
  emission_reductions <- estimate_emission_reductions(
    baseline,
    project,
    leakage,
    group_cols = "building_id"
  )
  emission_reductions_meta <- estimate_emission_reductions_ams_iie(
    baseline_data = annual_monitoring,
    project_data = annual_monitoring,
    leakage_data = annual_monitoring,
    group_cols = "building_id",
    leakage_args = list(leakage_col = "leakage_emissions_tco2e")
  )
}

For a full walk-through see the vignette in vignettes/cdmAmsIIe-methodology.Rmd.

Applicability Conditions

Projects must satisfy core AMS-II.E requirements before emission reductions can be claimed. Use the package helpers to document each criterion:

• check_applicability_energy_efficiency() – verifies the project delivers a meaningful reduction in energy intensity relative to a service indicator.
• check_applicability_fuel_switching() – ensures the blended emission factor of the project thermal fuel mix is no higher than the baseline mix when fuel switching is part of the activity.
• check_applicability_monitoring() – confirms monitoring datasets contain the required energy, service, and operating parameters without missing values.

Key Equations

cdmAmsIIe translates the numbered equations from AMS-II.E into composable R functions:

Equation	Function	Description
(1)	calculate_baseline_building_emissions()	Aggregates baseline thermal and electricity emissions for each building.
(2)	calculate_project_building_emissions()	Aggregates project thermal and electricity emissions after efficiency measures.
(3)	calculate_leakage_emissions()	Sums leakage components from upstream or market effects.
(4)	estimate_emission_reductions()	Combines baseline, project, and leakage emissions to obtain net emission reductions.

The meta-wrapper estimate_emission_reductions_ams_iie() chains these helpers for tidyverse-friendly datasets.

Monitoring and Simulation Utilities

• aggregate_monitoring_periods() summarises measured data across reporting periods while preserving building-level identifiers and numeric totals.
• simulate_ams_iie_dataset() generates example datasets with monitoring metadata, baseline and project parameters, and leakage placeholders to support tests, demos, and onboarding.